DetectingCherryPickedTrendlines/exp1_VLDBrev.py at master · UIC-InDeXLab/DetectingCherryPickedTrendlines · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
import Support
from Support import *
import basestuff
from basestuff import *
from time import time

'''
# ------- Oil ------------
print 'Oil:'
filename = "data/brent-daily.csv"
datecols = ['Date']
for n in [2000,4000,6000,8000,10000]:
    load_from_csv(filename,columns = ['Date','Price'],datecols=datecols,nrows=n)
    RoI_Split()
    print basestuff.n

    t = time()
    print support(-3,3)
    t1=time()-t

    t = time()
    print support_baseline(-3,3)
    t2=time()-t

    t = time()
    print support_ordered_enum(-3,3)
    t3=time()-t

    print t1,t2,t3
print "--------------------------"

# ------- CO2 ------------
print 'CO2:'
filename = "data/co2-ppm-daily.csv"
datecols = ['date']
for n in [2000,4000,6000,8000,10000]:
    load_from_csv(filename,columns = ['date','value'],datecols=datecols,nrows=n)
    RoI_Split()
    t = time()
    support(-100,100)
    t1=time()-t
    t = time()
    support_baseline(-100,100)
    t2=time()-t
    print basestuff.n,'t1',t1,'t2',t2

for n in [2000,4000,6000,8000,10000]:
    load_from_csv(filename,columns = ['date','value'],datecols=datecols,nrows=n)
    RoI_Split()
    t = time()
    support_ordered_enum(-100,100)
    t3=time()-t
    print basestuff.n,'t3',t3
print "--------------------------"
'''

# ------- wether ------------
print 'wether:'
datecols = ['datetime']
filename = "data/temperature.csv"
#n=1000
for n in [20000,30000,40000,50000]:
    load_from_csv(filename,columns = ["datetime",'New York'],datecols=datecols,nrows=n)
    RoI_Split()
    t = time()
    support(-1,1)
    t1=time()-t
    print t1
    t = time()
    #support_baseline(-1,1)
    t2=time()-t
    t = time()
    support_ordered_enum(-1,1)
    t3=time()-t
    print basestuff.n,t1,t2,t3
print "--------------------------"

# ------- DoT ------------
print 'DoT:'
filename = "data/DOTDec2017.csv"
#n=1000
for n in [100000,200000,300000,400000,500000]:
    load_from_csv(filename,columns = ['ORIGIN_AIRPORT_ID','DEST_AIRPORT_ID','DISTANCE'],nrows=n)
    RoI_Split()
    t = time()
    support(-100,100)
    t1=time()-t
    t = time()
    support_baseline(-1,1)
    t2=time()-t
    t = time()
    #support_ordered_enum(-100,100)
    t3=time()-t
    print basestuff.n,t1,t2,t3
print "--------------------------"

# ------- DoT2 ------------
print 'DoT Baseline:'
filename = "data/DOTDec2017.csv"
#n=1000
for n in [100000,200000,300000,400000,500000]:
    load_from_csv(filename,columns = ['ORIGIN_AIRPORT_ID','DEST_AIRPORT_ID','DISTANCE'],nrows=n)
    RoI_Split()
    t = time()
    support_baseline(-100,100)
    t2=time()-t
    print basestuff.n,t2
print "--------------------------"


# ------- Stock ------------
print 'stock:'
#load_from_csv("data/stock_sample.csv",["ticker","open","close","low","high","volume","date"])
datecols = ['date']
filename = "data/historical_stock_prices.csv"
#n=1000
for n in [100,1000,10000]:
    load_from_csv(filename,["date","close"],datecols=datecols,nrows=n)
    RoI_Split()
    t = time()
    support(-1,1)
    t1=time()-t
    t = time()
    support_baseline(-1,1)
    t2=time()-t
    t = time()
    support_ordered_enum(-1,1)
    t3=time()-t
    print basestuff.n,t1,t2,t3
for n in [100000,1000000,10000000]:
    load_from_csv(filename,["date","close"],datecols=datecols,nrows=n)
    RoI_Split()
    t = time()
    support(-1,1)
    t1=time()-t
    print basestuff.n,t1
print "--------------------------"

#RoI_S([(None,"'2013-08-02 0:00'")], [("'2013-08-03 0:00'",None)])


#for i in range(10):
#print support_rand(10, 50, 10)s